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Center for Nonlinear Studies |
Data assimilation techniques combine information extracted from a model representation of a dynamical system, observational data, and error statistics to produce an optimal estimate of the evolving state of the system. The development of the adjoint of the forecast model and of the adjoint of the data assimilation system (adjoint-DAS) makes feasible the evaluation of the local sensitivity of a model forecast aspect with respect to a large number of parameters in the DAS. Theoretical and practical aspects of the adjoint-DAS sensitivity analysis are discussed in the context of variational data assimilation for numerical weather prediction (NWP). Observation sensitivity and forecast impact assessment, diagnostics, and optimization of error covariance parameters in a multi-sensor observing system are presented from the proof-of-concept stage to the current status of implementation at NWP centers.
Host: Humberto C Godinez Vazquez, Mathematical Modeling and Analysis Theoretical Division, 5-9188